While promising, controllability of the brain is still an underexplored research field due to the different ways in which controllability can be implemented [1]}.
In practice, controlling the whole system from a single node could not befeasible due to numerical errors even in relatively small networks of hundreds of nodes. Furthermore, the energy of the control signal might be too high to be generated physically or destructive for the network functioning itself [2]}, [3]}.
A possible solution would be to focus on specific parts of the network so as to reduce the overall computational complexity [4]}, [5]}.
Determining how single nodes can affect network's subsystems still remains poorly understood in the human brain. Furthermore, current results have neglected the fact that brain controllability could vary with aging, which induces progressive changes in the underlying network structure or with focal brain damages [6]}, [7]}.
